Most recently, deformable convolution (DConv) [1]} has been investigated in VFI task to warp features and frames. Gui et al. [2]} propose the Featureflow, which regards DConv as the universal version of motion flows. Different from the one-to-one flow warping, DConv layer produces multiple offsets for each pixel, and uses a weighted average of them to predict target pixel. Thus DConv's offsets warping can be considered as many-to-one flow warping. Theoretically, DConv methods can collect more diverse information and produce more complex transformations, which should be more robust than single optical flow methods when handling the complex motions.
However, in practice, the increased degree of freedom makes the training of the DConv model more difficult, which limits their performance [3]}, [4]}.
